The present invention relates to modifying a synthetically generated assay. In particular, the modification is done by using whole crude properties.
Within the petrochemical industry, there are many instances where a very detailed analysis of a process feed or distillation product is needed for the purpose of making business decisions, planning, controlling and optimizing operations and certifying products. Such a detailed analysis will be referred to as an assay, a wet crude oil assay being one specific example. Since a detailed assay is both costly and time consuming to perform, it is desirable to have a surrogate methodology that can provide the information of the detailed analysis inexpensively and in a timely fashion. Techniques such as those involving NMR, UV, visible and near and mid infrared spectroscopy can provide information of varying degrees of detail, inexpensively and in a timely fashion. This information can then be used to synthesis an estimate of the detailed assay, the Virtual Assay.
The quality of the predictions of the various assay properties made using these Virtual Assay synthesis techniques can vary considerably as a function of the specific analytical technique employed to generate the Virtual Assay as well as the quality, scope and specific blend of reference materials that are used. For example, various forms of NMR, near, or mid range IR spectroscopies are sensitive to particular types of molecules or molecular functional groups. Thus these spectroscopies can provide information on certain molecules or molecular types, but they do not directly measure such properties as molecular weight or boiling point, nor are they sensitive to trace level elements such as metals (e.g. Ni, V or Fe) or trace level species (e.g. mercaptans).
Therefore, predictions of the level of these properties, elements or compounds can be less accurate and is simply a function of the surrogate selected or blends of reference materials used to match the spectra and other key measured properties. The reference materials used to generate the blend also determine the accuracy of the blend. If the blend is comprised of materials which are very similar to the target material being analyzed, based on similar geological, chemical formulations or physical location, then the accuracy of the prediction may be sufficient to meet the required business objectives. However, if the blend is comprised of materials, which are substantially different than the target material being analyzed, then the prediction of these properties could vary significantly from the actual levels present in the target material.
Therefore, an ability to improve the Virtual Assay prediction made based on these analytical techniques would be extremely valuable to making better business decisions. The current invention provides such an improved ability.
Current state of the art as reported in the literature, includes but is not limited to analytical techniques involving NMR, UV, visible and near mid infrared spectroscopy. Examples include:
Infrared and Raman spectroscopies have been employed for process analysis of a variety of petrochemical streams. G. M. Hieftje, D. E. Honigs and T. B. Hirschfeld (U.S. Pat. No. 4,800,279 Jan. 24, 1989) described the prediction of physical properties for simple hydrocarbon mixtures from near-infrared (NIR) spectra using multiple linear regression (MLR). D. A. Swinkels, P. M. Fredricks and P. R. Osborn applied FT-IR and Principal Components Regression (PCR) to the analysis of coals (U.S. Pat. No. 4,701,838 Oct. 20, 1987). J. M. Brown (U.S. Pat. No. 5,121,337 Jun. 9, 1992) describes a method for predicting property and composition data of samples using spectra and Constrained Principal Spectra Analysis (CPSA). R. Clarke describes a method for measuring properties of hydrocarbons using Raman spectroscopy (U.S. Pat. No. 5,139,334 Aug. 18, 1992). R. H. Clarke and D. Tang describe a method and mid-infrared apparatus for determining hydrocarbon fuel properties (U.S. Pat. No. 5,225,679 Jul. 6, 1993). D. C. Lambert and A. Martens (EP 2852521 and U.S. Pat. No. 5,490,085 Feb. 6, 1996) describe the prediction of octane number using NIR spectra and MLR, as does S. M. Maggard (U.S. Pat. No. 4,963,745 Oct. 16, 1990). Maggard also describes the estimation of paraffins, isoparaffins, aromatics, naphthenes and olefins in gasolines using NIR and MLR or Partial Least Squares (PLS) (U.S. Pat. No. 5,349,188 Sep. 20, 1994), the prediction of blend properties from the spectra of blend components using NIR and MLR (U.S. Pat. No. 5,223,714 Jun. 29, 1993), and the prediction of oxygenates and oxygen content of gasolines using NIR spectra. S. Maggard and W. T. Welch discuss prediction of organic sulfur content for mid-distillate fuels using NIR spectra (U.S. Pat. No. 5,348,645 Sep. 20, 1994). J. B. Cooper, M. B. Sumner; W. T. Welch and K. L Wise describe a method for measuring oxygen and oxygenate content of gasolines using Raman spectroscopy (U.S. Pat. No. 5,596,196 Feb. 21, 1997). R. R. Bledsoe, J. B. Cooper, M. B. Sumner; W. T. Welch, B. K. Wilt and K. L. Wise describe a method of predicting octane number and Reid vapor pressure of gasolines using Raman spectroscopy (U.S. Pat. No. 5,892,228 Apr. 6, 1999). These methods typically involve linear models for individual properties, and are thus not necessarily useful for properties that are nonlinear functions of composition, nor for prediction of properties of subfractions of the sample being analyzed. While they can provide rapid analyses on minimal sample volumes, their application for detailed analyses would require the development and maintenance of an impracticably large number of models. In addition, many of these NIR methods operate in spectral regions where crude oil is essentially opaque. Raman methods are typically not applicable to crude oils or other heavy hydrocarbons due to interferences from fluorescence.
Espinosa, A. Martens, G. Ventron, D. C. Lambert and A. Pasquier (EP 305090 and U.S. Pat. No. 5,475,612 Dec. 12, 1995) describe predicting physical properties of blends from near-infrared spectra of blend components using MLR. Products and ratios of absorbances were included in an attempt to predict nonlinear properties such as RON. A. Espinosa, D. C. Lambert, A. Martens and G. Ventron (EP 304232 and U.S. Pat. No. 5,452,232 Apr. 25, 1990) describe a method for predicting properties of process products from spectra of process feeds using NIR and MLR. Products and ratios of absorbances were again used to handle nonlinear properties. B. N. Perry and J. M. Brown describe a method for improving the prediction of nonlinear properties by post-processing results from linear models (U.S. Pat. No. 5,641,962 Jun. 24, 1997). J. M. Tolchard and A. Boyd (WO9417391) describe the use of NIR and neural networks for the prediction of hydrocarbon physical properties. While these methods could potentially be use to predict properties that have nonlinear relationships to composition, all would require that separate models be built for each property to be predicted, and are thus impractical for assay synthesis.
R. DiFoggio, M. Sadhukhan and M. Ranc (U.S. Pat. No. 5,360,972 Nov. 1, 1994) describe a method for estimating physical properties of a material using a combination of infrared data and data indicative of trace level compounds. DiFoggio et. al. do not teach the use of infrared and inspection data, and their method would require separate models to be built for each property to be estimated.
Other methodologies have been employed for detailed analyses of hydrocarbons. T. R. Ashe, R. W. Kapala and G. Roussis (U.S. Pat. No. 5,699,270 Dec. 16, 1997) employed PLS models of GC/MS data to predict chemical, performance, perceptual and physical properties of feed and product streams from various steps in lubricating oil manufacturing. T. R. Ashe, S. G. Roussis, J. W. Fedora, G. Felshy and W. P. Fitzgerald (U.S. Pat. No. 5,699,269 Dec. 16, 1997) used PLS models of GC/MS data to predict physical and chemical properties of crude oils. Both method employed separate models for each property predicted.
I. H. Cho, J. G. Choi and H. I. Chung (WO 00/39561) described an apparatus that combined a distillation unit and a spectrometer for analysis of crude oils. Separate chemometric models were employed for each property for each distillate cut.
K. Hidajat and S. M. Chong claim to measure total boiling point and density of crude oils from NIR spectra (J. Near Infrared Spectroscopy 8, 53-59 (2000)). Neither other whole crude properties, nor properties of distillate cuts were predicted.
PROCESS MRA by Invensys—R. W. Karg and T. A. Clinkscales (WO 01/51588) describe a method for using NMR to control a petroleum distillation process. R. W. Karg and T. A. Clinkscales and C. Swart (WO 01/70912) describe a method of using NMR to control crude blending. Neither method provides a complete synthetic assay. In particular, the methods do not describe the prediction of sulfur, acid number, metal content or trace components.
TOPNIR by Intertek/CalebBrett—B. Descales, D. Lambert, J. LLinas, A. Martens, S. Osta, M. Sanchez and S. Bages (U.S. Pat. No. 6,070,128 May 30, 2000) describe a topology based method for determining properties from NIR spectra. Their method calculates an Euclidean distance between the spectrum of the sample being analyzed and all of the reference spectra in the database. Reference samples whose spectra fall within a predetermined distance of the unknown spectra are selected, and the properties of the unknown are calculated as the average of the properties of the selected references. Alternatively, the spectrum of the unknown can be fit as a linear combination of the selected references, and the properties of the unknown calculated as the weighted combination of the reference sample properties. Nonlinear properties are handled through blending factors. If there are insufficient references within the predetermined distance of the unknown, the method provides a means of densifying the database to interpolate between the reference samples. While the method of Descales, et. al. can be used to analyze the unknown as if it were a blend of the reference samples, the blend components are limited to those samples who have spectra nearly identical to the spectrum of the unknown, i.e. the nearest neighbors in the spectral space.
Petrobras NIR—A. F. Bueno described the use of NIR for crude oil characterization (http://www.sbqclaq.sbq.org.br/celio/pdf/Aerenton.pdf, Pittsburgh Conference 2004, paper 20600-300). Only distillation and a limited number of physical properties were predicted.
Various of these techniques could be used to generate some or all of the data for a synthesized assay which is the starting point for the application of the methodology of this invention. However, the preferred method of generating a Virtual Assay is the technique described by J. M. Brown, U.S. Pat. No. 6,662,116 B2, Dec. 9, 2003, “Method for Analyzing an Unknown Material as a Blend of Known Materials Calculated so as to Match Certain Analytical Data and Predicting Properties of the Unknown Based on the Calculated Blend” (hereinafter referred to a “EM virtual assay”).
Some of these techniques either select the closest match based on a set of predetermined criteria from a library of crude oils, or develop a blend of materials, which match the infrared spectra, and other key measured properties in the case of the Brown U.S. Pat. No. 6,662,116. The calculated blend of the reference materials is then used to predict additional chemical and physical properties of the unknown using the measured chemical and physical properties of the reference materials and known blending relationships.
None of these existing techniques have used directly measured property values to adjust the predicted or blended whole crude and distributed values resulting from the analytical tests that this invention covers.